






"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE   =   {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Union[str, Any]      =  """dpt"""
					def __init__(							self							:	int     , UpperCAmelCase							:	Tuple=768     , UpperCAmelCase							:	List[Any]=12     , UpperCAmelCase							:	Tuple=12     , UpperCAmelCase							:	Dict=3072     , UpperCAmelCase							:	str="gelu"     , UpperCAmelCase							:	Dict=0.0     , UpperCAmelCase							:	Dict=0.0     , UpperCAmelCase							:	Any=0.0_2     , UpperCAmelCase							:	Optional[int]=1e-1_2     , UpperCAmelCase							:	Union[str, Any]=384     , UpperCAmelCase							:	Optional[Any]=16     , UpperCAmelCase							:	List[str]=3     , UpperCAmelCase							:	Union[str, Any]=False     , UpperCAmelCase							:	str=True     , UpperCAmelCase							:	Optional[int]=[2, 5, 8, 11]     , UpperCAmelCase							:	Optional[int]="project"     , UpperCAmelCase							:	Tuple=[4, 2, 1, 0.5]     , UpperCAmelCase							:	List[str]=[96, 192, 384, 768]     , UpperCAmelCase							:	Optional[Any]=256     , UpperCAmelCase							:	Any=-1     , UpperCAmelCase							:	int=False     , UpperCAmelCase							:	Union[str, Any]=True     , UpperCAmelCase							:	Tuple=0.4     , UpperCAmelCase							:	List[Any]=255     , UpperCAmelCase							:	List[str]=0.1     , UpperCAmelCase							:	List[str]=[1, 1024, 24, 24]     , UpperCAmelCase							:	Dict=[0, 1]     , UpperCAmelCase							:	int=None     , **UpperCAmelCase							:	Optional[int]     , )	->       List[str]:
											super().__init__(**UpperCAmelCase   )

											lowerCAmelCase   :int						=			hidden_size
											lowerCAmelCase   :int						=			is_hybrid

											if self.is_hybrid:
																	if backbone_config is None:
																							logger.info('Initializing the config with a `BiT` backbone.'   )
																							lowerCAmelCase   :int						=			{
																							    'global_padding': 'same',
																							    'layer_type': 'bottleneck',
																							    'depths': [3, 4, 9],
																							    'out_features': ['stage1', 'stage2', 'stage3'],
																							    'embedding_dynamic_padding': True,
																							}
																							lowerCAmelCase   :Any						=			BitConfig(**UpperCAmelCase   )
																	elif isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							logger.info('Initializing the config with a `BiT` backbone.'   )
																							lowerCAmelCase   :List[str]						=			BitConfig(**UpperCAmelCase   )
																	elif isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							lowerCAmelCase   :Any						=			backbone_config
																	else:
																							raise ValueError(
																							    f"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""   )

																	lowerCAmelCase   :List[str]						=			backbone_featmap_shape
																	lowerCAmelCase   :Optional[int]						=			neck_ignore_stages

																	if readout_type != "project":
																							raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'   )
											else:
																	lowerCAmelCase   :List[Any]						=			None
																	lowerCAmelCase   :Any						=			None
																	lowerCAmelCase   :Tuple						=			[]

											lowerCAmelCase   :List[Any]						=			num_hidden_layers
											lowerCAmelCase   :Optional[Any]						=			num_attention_heads
											lowerCAmelCase   :Optional[Any]						=			intermediate_size
											lowerCAmelCase   :int						=			hidden_act
											lowerCAmelCase   :List[str]						=			hidden_dropout_prob
											lowerCAmelCase   :Optional[Any]						=			attention_probs_dropout_prob
											lowerCAmelCase   :List[Any]						=			initializer_range
											lowerCAmelCase   :int						=			layer_norm_eps
											lowerCAmelCase   :Optional[int]						=			image_size
											lowerCAmelCase   :Dict						=			patch_size
											lowerCAmelCase   :Tuple						=			num_channels
											lowerCAmelCase   :List[str]						=			qkv_bias
											lowerCAmelCase   :Union[str, Any]						=			backbone_out_indices
											if readout_type not in ["ignore", "add", "project"]:
																	raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'   )
											lowerCAmelCase   :Optional[Any]						=			readout_type
											lowerCAmelCase   :Optional[Any]						=			reassemble_factors
											lowerCAmelCase   :str						=			neck_hidden_sizes
											lowerCAmelCase   :str						=			fusion_hidden_size
											lowerCAmelCase   :Union[str, Any]						=			head_in_index
											lowerCAmelCase   :List[str]						=			use_batch_norm_in_fusion_residual
											# auxiliary head attributes (semantic segmentation)
											lowerCAmelCase   :List[Any]						=			use_auxiliary_head
											lowerCAmelCase   :str						=			auxiliary_loss_weight
											lowerCAmelCase   :Tuple						=			semantic_loss_ignore_index
											lowerCAmelCase   :str						=			semantic_classifier_dropout
					def        UpperCAmelCase__ (							self							:	Any   )	->       int:
											lowerCAmelCase   :List[str]						=			copy.deepcopy(self.__dict__   )

											if output["backbone_config"] is not None:
																	lowerCAmelCase   :int						=			self.backbone_config.to_dict()

											lowerCAmelCase   :Any						=			self.__class__.model_type
											return output







"""simple docstring"""


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters() ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is False
																		), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is True
																		), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__=True ):





						'''simple docstring'''






						model.train()
						lowerCAmelCase   :Dict						=			model(a__ )
						lowerCAmelCase   :Union[str, Any]						=			F.mse_loss(a__     ,      target.to(output.device ) )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(a__ )


def        UpperCAmelCase      (					a__     ,      a__=False ):





						'''simple docstring'''






						set_seed(42 )
						lowerCAmelCase   :Any						=			RegressionModel()
						lowerCAmelCase   :List[str]						=			deepcopy(a__ )
						lowerCAmelCase   :str						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						model.to(accelerator.device )
						if sched:
												lowerCAmelCase   :List[Any]						=			AdamW(params=model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Dict						=			AdamW(params=ddp_model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Tuple						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
												lowerCAmelCase   :Optional[Any]						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
						# Make a copy of `model`
						if sched:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :int						=			accelerator.prepare(a__     ,      a__     ,      a__     ,      a__ )
						else:
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			accelerator.prepare(a__     ,      a__ )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :Any						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad     ,      ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Any						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Dict						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :str						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :int						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :str						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			get_training_setup(a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Dict						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Any						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Optional[int]						=			ddp_input[torch.randperm(len(a__ ) )]
						GradientState._reset_state()


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			get_training_setup(a__     ,      a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
												lowerCAmelCase   :int						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
												if accelerator.num_processes > 1:
																		check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
						GradientState._reset_state()


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Accelerator()

						lowerCAmelCase   :int						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase   :Any						=			RegressionDataset(length=96 )
						lowerCAmelCase   :Dict						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase	, lowerCAmelCase   :Tuple						=			accelerator.prepare(a__     ,      a__ )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(a__ ):
												assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
												if iteration < len(a__ ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(a__ ):
																														assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
																														if batch_num < len(a__ ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			Accelerator()
						lowerCAmelCase   :Optional[int]						=			accelerator.state
						if state.local_process_index == 0:
												print('**Test `accumulate` gradient accumulation with dataloader break**' )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print('**Test NOOP `no_sync` context manager**' )
												test_noop_sync(a__ )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print('**Test Distributed `no_sync` context manager**' )
												test_distributed_sync(a__ )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    '**Test `accumulate` gradient accumulation, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																								test_gradient_accumulation(a__     ,      a__ )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version('<'     ,      '2.0' ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      '`split_batches=False`, `dispatch_batches=False`**'     ,      )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																														test_gradient_accumulation_with_opt_and_scheduler(a__     ,      a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()